In pattern recognition, “Pattern classification, Richard O. Duda, Peter, E. Hart, David G. Stork, Wiley-Interscience” has disclosed a method of reducing memory of a feature vector, suppressing degradation of recognition performance.
Application of this reduction method provides a subspace where sum of the square error of the feature vector approximation by projection is at a minimum. Projection on the subspace allows us to reduce dimension of the feature vector and memory, keeping the square error of the whole feature vector small. Unlike a data compression, distance and angle between the feature vectors can be calculated in an approximate state without returning to the original condition.
However, the above mentioned reduction method poses a problem that the amount of memory of the feature vectors may not be sharply reduced, suppressing degradation of recognition performance, since the dimension of the subspace to be projected needs to be remained to some extent in order to maintain recognition performance.